Review article: Pre‐hospital provider clinical judgement upon arrival to the emergency department : A systematic review and meta‐analysis

Prediction models in prehospital and emergency medicine research: How to derive and internally validate a clinical prediction model

Clinical prediction models are created to help clinicians with medical decision making, aid in risk stratification, and improve diagnosis and/or prognosis. With growing availability of both prehospital and in-hospital observational registries and electronic health records, there is an opportunity to develop, validate, and incorporate prediction models into clinical practice. However, many prediction models have high risk of bias due to poor methodology. Given that there are no methodological standards aimed at developing prediction models specifically in the prehospital setting, the objective of this paper is to describe the appropriate methodology for the derivation and validation of clinical prediction models in this setting. What follows can also be applied to the emergency medicine (EM) setting. There are eight steps that should be followed when developing and internally validating a prediction model: (1) problem definition, (2) coding of predictors, (3) addressing missing data, (4) ensuring adequate sample size, (5) variable selection, (6) evaluating model performance, (7) internal validation, and (8) model presentation. Subsequent steps include external validation, assessment of impact, and cost-effectiveness. By following these steps, researchers can develop a prediction model with the methodological rigor and quality required for prehospital and EM research.

Performance of early warning and risk stratification scores versus clinical judgement in the acute setting: a systematic review

OBJECTIVE

Risk stratification is increasingly based on Early Warning Score (EWS)-based models, instead of clinical judgement. However, it is unknown how risk-stratification models and EWS perform as compared with the clinical judgement of treating acute healthcare providers. Therefore, we performed a systematic review of all available literature evaluating clinical judgement of healthcare providers to the use of risk-stratification models in predicting patients' clinical outcome.

METHODS

Studies comparing clinical judgement and risk-stratification models in predicting outcomes in adult patients presenting at the ED were eligible for inclusion. Outcomes included the need for intensive care unit (ICU) admission; severe adverse events; clinical deterioration and mortality. Risk of bias among the included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool.

RESULTS

Six studies (6419 participants) were included of which 4 studies were judged to be at high risk of bias. Only descriptive analysis was performed as a meta-analysis was not possible due to few included studies and high clinical heterogeneity. The performance of clinical judgement and risk-stratification models were both moderate in predicting mortality, deterioration and need for ICU admission with area under the curves between 0.70 and 0.89. The performance of clinical judgement did not significantly differ from risk-stratification models in predicting mortality (n=2 studies) or deterioration (n=1 study). However, clinical judgement of healthcare providers was significantly better in predicting the need for ICU admission (n=2) and severe adverse events (n=1 study) as compared with risk-stratification models.

CONCLUSION

Based on limited existing data, clinical judgement has greater accuracy in predicting the need for ICU admission and the occurrence of severe adverse events compared with risk-stratification models in ED patients. However, performance is similar in predicting mortality and deterioration.

PROSPERO REGISTRATION NUMBER

CRD42020218893.

Paramedic streaming upon arrival in emergency department: A prospective study

OBJECTIVE

The role of paramedics in hospital triage or streaming models has not been adequately explored and is potentially a missed opportunity for enhanced patient flow. The aim of the present study was to assess the concordance between a streaming decision by paramedics with the decision by nurses after arrival to the ED.

METHODS

A prospective observational study was conducted. Paramedics were met at the entrance to the hospital and asked which destination they thought was appropriate (the index test). The ED nurse streaming decision was the reference standard. Cases of discordance were reviewed and assessed for clinical risk by an independent expert panel that was blinded.

RESULTS

We collected data from 500 cases that were transported by ambulance consisting of 55% males with a median age of 57 years (interquartile range 38-75). The overall concordance between paramedics' and streaming decision was 86.4% (95% confidence interval 83.1-89.1). The concordance was highest among patients streamed to resuscitation and general cubicles. Among discordant cases (n = 68), 39 were streamed to a more acute destination than the paramedic suggested. Of the 68 discordant cases, 56 were deemed to be of no clinical risk.

CONCLUSIONS

Despite limited knowledge of patient load within the ED, paramedics can allocate a streaming destination with high accuracy and this appears to be associated with low clinical risks. Early pre-hospital notification of streaming destination with proactive allocation of ED destination presents a real opportunity to minimise off-load times and improve patient flow.